1-3. 度数分布とヒストグラム
library(plotly)
#性別T1の度数分布とヒストグラム
gender_T1_count <- dplyr::count(data, gender_T1)
knitr::kable(gender_T1_count) #テーブル化
a <- ggplot(data = data, mapping = aes(x = gender_T1, fill = factor(gender_T1))) + geom_bar() #視覚化
ggplotly(a) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#性別T2の度数分布とヒストグラム
gender_T2_count <- dplyr::count(data, gender_T2)
knitr::kable(gender_T2_count) #テーブル化
b <- ggplot(data = data, mapping = aes(x = gender_T2, fill = factor(gender_T2))) + geom_bar() #視覚化
ggplotly(b) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#性別T3の度数分布とヒストグラム
gender_T3_count <- dplyr::count(data, gender_T3)
knitr::kable(gender_T3_count) #テーブル化
c <- ggplot(data = data, mapping = aes(x = gender_T3, fill = factor(gender_T3))) + geom_bar() #視覚化
ggplotly(c) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#性別T4の度数分布とヒストグラム
gender_T4_count <- dplyr::count(data, gender_T4)
knitr::kable(gender_T4_count) #テーブル化
d <- ggplot(data = data, mapping = aes(x = gender_T4, fill = factor(gender_T4))) + geom_bar() #視覚化
ggplotly(d) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc1_T1の度数分布とヒストグラム
hsc1_T1_count <- dplyr::count(data, hsc1_T1)
knitr::kable(hsc1_T1_count) #テーブル化
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e <- ggplot(data = data, mapping = aes(x = hsc1_T1, fill = factor(hsc1_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(e) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc2_T1の度数分布とヒストグラム
hsc2_T1_count <- dplyr::count(data, hsc2_T1)
knitr::kable(hsc2_T1_count) #テーブル化
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f <- ggplot(data = data, mapping = aes(x = hsc2_T1, fill = factor(hsc2_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(f) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc3_T1の度数分布とヒストグラム
hsc3_T1_count <- dplyr::count(data, hsc3_T1)
knitr::kable(hsc3_T1_count) #テーブル化
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g <- ggplot(data = data, mapping = aes(x = hsc3_T1, fill = factor(hsc3_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(g) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc4_T1の度数分布とヒストグラム
hsc4_T1_count <- dplyr::count(data, hsc4_T1)
knitr::kable(hsc4_T1_count) #テーブル化
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h <- ggplot(data = data, mapping = aes(x = hsc4_T1, fill = factor(hsc4_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(h) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc5_T1の度数分布とヒストグラム
hsc5_T1_count <- dplyr::count(data, hsc5_T1)
knitr::kable(hsc5_T1_count) #テーブル化
i <- ggplot(data = data, mapping = aes(x = hsc5_T1, fill = factor(hsc5_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(i) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc6_T1の度数分布とヒストグラム
hsc6_T1_count <- dplyr::count(data, hsc6_T1)
knitr::kable(hsc6_T1_count) #テーブル化
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j <- ggplot(data = data, mapping = aes(x = hsc6_T1, fill = factor(hsc6_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(j) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc7_T1の度数分布とヒストグラム
hsc7_T1_count <- dplyr::count(data, hsc7_T1)
knitr::kable(hsc7_T1_count) #テーブル化
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k <- ggplot(data = data, mapping = aes(x = hsc7_T1, fill = factor(hsc7_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(k) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc8_T1の度数分布とヒストグラム
hsc8_T1_count <- dplyr::count(data, hsc8_T1)
knitr::kable(hsc8_T1_count) #テーブル化
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l <- ggplot(data = data, mapping = aes(x = hsc8_T1, fill = factor(hsc8_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(l) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc9_T1の度数分布とヒストグラム
hsc9_T1_count <- dplyr::count(data, hsc9_T1)
knitr::kable(hsc9_T1_count) #テーブル化
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m <- ggplot(data = data, mapping = aes(x = hsc9_T1, fill = factor(hsc9_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(m) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc10_T1の度数分布とヒストグラム
hsc10_T1_count <- dplyr::count(data, hsc10_T1)
knitr::kable(hsc10_T1_count) #テーブル化
n <- ggplot(data = data, mapping = aes(x = hsc10_T1, fill = factor(hsc10_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(n) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc11_T1の度数分布とヒストグラム
hsc11_T1_count <- dplyr::count(data, hsc11_T1)
knitr::kable(hsc11_T1_count) #テーブル化
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o <- ggplot(data = data, mapping = aes(x = hsc11_T1, fill = factor(hsc11_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(o) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc12_T1の度数分布とヒストグラム
hsc12_T1_count <- dplyr::count(data, hsc12_T1)
knitr::kable(hsc12_T1_count) #テーブル化
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p <- ggplot(data = data, mapping = aes(x = hsc12_T1, fill = factor(hsc12_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(p) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc1_T2の度数分布とヒストグラム
hsc1_T2_count <- dplyr::count(data, hsc1_T2)
knitr::kable(hsc1_T2_count) #テーブル化
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q <- ggplot(data = data, mapping = aes(x = hsc1_T2, fill = factor(hsc1_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(q) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc2_T2の度数分布とヒストグラム
hsc2_T2_count <- dplyr::count(data, hsc2_T2)
knitr::kable(hsc2_T2_count) #テーブル化
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r <- ggplot(data = data, mapping = aes(x = hsc2_T2, fill = factor(hsc2_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(r) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc3_T2の度数分布とヒストグラム
hsc3_T2_count <- dplyr::count(data, hsc3_T2)
knitr::kable(hsc3_T2_count) #テーブル化
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| NA |
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s <- ggplot(data = data, mapping = aes(x = hsc3_T2, fill = factor(hsc3_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(s) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc4_T2の度数分布とヒストグラム
hsc4_T2_count <- dplyr::count(data, hsc4_T2)
knitr::kable(hsc4_T2_count) #テーブル化
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| NA |
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t <- ggplot(data = data, mapping = aes(x = hsc4_T2, fill = factor(hsc4_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(t) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc5_T2の度数分布とヒストグラム
hsc5_T2_count <- dplyr::count(data, hsc5_T2)
knitr::kable(hsc5_T2_count) #テーブル化
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| NA |
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u <- ggplot(data = data, mapping = aes(x = hsc5_T2, fill = factor(hsc5_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(u) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc6_T2の度数分布とヒストグラム
hsc6_T2_count <- dplyr::count(data, hsc6_T2)
knitr::kable(hsc6_T2_count) #テーブル化
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v <- ggplot(data = data, mapping = aes(x = hsc6_T2, fill = factor(hsc6_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(v) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc7_T2の度数分布とヒストグラム
hsc7_T2_count <- dplyr::count(data, hsc7_T2)
knitr::kable(hsc7_T2_count) #テーブル化
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w <- ggplot(data = data, mapping = aes(x = hsc7_T2, fill = factor(hsc7_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(w) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc8_T2の度数分布とヒストグラム
hsc8_T2_count <- dplyr::count(data, hsc8_T2)
knitr::kable(hsc8_T2_count) #テーブル化
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neko <- ggplot(data = data, mapping = aes(x = hsc8_T2, fill = factor(hsc8_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(neko) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc9_T2の度数分布とヒストグラム
hsc9_T2_count <- dplyr::count(data, hsc9_T2)
knitr::kable(hsc9_T2_count) #テーブル化
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| NA |
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y <- ggplot(data = data, mapping = aes(x = hsc9_T2, fill = factor(hsc9_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(y) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc10_T2の度数分布とヒストグラム
hsc10_T2_count <- dplyr::count(data, hsc10_T2)
knitr::kable(hsc10_T2_count) #テーブル化
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| NA |
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z <- ggplot(data = data, mapping = aes(x = hsc10_T2, fill = factor(hsc10_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(z) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc11_T2の度数分布とヒストグラム
hsc11_T2_count <- dplyr::count(data, hsc11_T2)
knitr::kable(hsc11_T2_count) #テーブル化
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aa <- ggplot(data = data, mapping = aes(x = hsc11_T2, fill = factor(hsc11_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(aa) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc12_T2の度数分布とヒストグラム
hsc12_T2_count <- dplyr::count(data, hsc12_T2)
knitr::kable(hsc12_T2_count) #テーブル化
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bb <- ggplot(data = data, mapping = aes(x = hsc12_T2, fill = factor(hsc12_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(bb) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc1_T3の度数分布とヒストグラム
hsc1_T3_count <- dplyr::count(data, hsc1_T3)
knitr::kable(hsc1_T3_count) #テーブル化
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cc <- ggplot(data = data, mapping = aes(x = hsc1_T3, fill = factor(hsc1_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(cc) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc2_T3の度数分布とヒストグラム
hsc2_T3_count <- dplyr::count(data, hsc2_T3)
knitr::kable(hsc2_T3_count) #テーブル化
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| NA |
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dd <- ggplot(data = data, mapping = aes(x = hsc2_T3, fill = factor(hsc2_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(dd) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc3_T3の度数分布とヒストグラム
hsc3_T3_count <- dplyr::count(data, hsc3_T3)
knitr::kable(hsc3_T3_count) #テーブル化
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| NA |
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ff <- ggplot(data = data, mapping = aes(x = hsc3_T3, fill = factor(hsc3_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ff) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc4_T3の度数分布とヒストグラム
hsc4_T3_count <- dplyr::count(data, hsc4_T3)
knitr::kable(hsc4_T3_count) #テーブル化
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| NA |
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gg <- ggplot(data = data, mapping = aes(x = hsc4_T3, fill = factor(hsc4_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(gg) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc5_T3の度数分布とヒストグラム
hsc5_T3_count <- dplyr::count(data, hsc5_T3)
knitr::kable(hsc5_T3_count) #テーブル化
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| NA |
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hh <- ggplot(data = data, mapping = aes(x = hsc5_T3, fill = factor(hsc5_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(hh) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc6_T3の度数分布とヒストグラム
hsc6_T3_count <- dplyr::count(data, hsc6_T3)
knitr::kable(hsc6_T3_count) #テーブル化
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| NA |
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ii <- ggplot(data = data, mapping = aes(x = hsc6_T3, fill = factor(hsc6_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ii) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc7_T3の度数分布とヒストグラム
hsc7_T3_count <- dplyr::count(data, hsc7_T3)
knitr::kable(hsc7_T3_count) #テーブル化
| 1 |
5 |
| 2 |
11 |
| 3 |
8 |
| 4 |
22 |
| 5 |
13 |
| 6 |
23 |
| 7 |
23 |
| NA |
9 |
jj <- ggplot(data = data, mapping = aes(x = hsc7_T3, fill = factor(hsc7_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(jj) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc8_T3の度数分布とヒストグラム
hsc8_T3_count <- dplyr::count(data, hsc8_T3)
knitr::kable(hsc8_T3_count) #テーブル化
| 1 |
2 |
| 2 |
5 |
| 3 |
15 |
| 4 |
23 |
| 5 |
29 |
| 6 |
18 |
| 7 |
13 |
| NA |
9 |
kk <- ggplot(data = data, mapping = aes(x = hsc8_T3, fill = factor(hsc8_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(kk) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc9_T3の度数分布とヒストグラム
hsc9_T3_count <- dplyr::count(data, hsc9_T3)
knitr::kable(hsc9_T3_count) #テーブル化
| 1 |
6 |
| 2 |
15 |
| 3 |
16 |
| 4 |
29 |
| 5 |
25 |
| 6 |
10 |
| 7 |
4 |
| NA |
9 |
ll <- ggplot(data = data, mapping = aes(x = hsc9_T3, fill = factor(hsc9_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ll) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc10_T3の度数分布とヒストグラム
hsc10_T3_count <- dplyr::count(data, hsc10_T3)
knitr::kable(hsc10_T3_count) #テーブル化
| 3 |
1 |
| 4 |
3 |
| 5 |
10 |
| 6 |
29 |
| 7 |
62 |
| NA |
9 |
dog <- ggplot(data = data, mapping = aes(x = hsc10_T3, fill = factor(hsc10_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(dog) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc11_T3の度数分布とヒストグラム
hsc11_T3_count <- dplyr::count(data, hsc11_T3)
knitr::kable(hsc11_T3_count) #テーブル化
| 1 |
3 |
| 2 |
4 |
| 3 |
10 |
| 4 |
22 |
| 5 |
25 |
| 6 |
25 |
| 7 |
16 |
| NA |
9 |
mm <- ggplot(data = data, mapping = aes(x = hsc11_T3, fill = factor(hsc11_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(mm) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc12_T3の度数分布とヒストグラム
hsc12_T3_count <- dplyr::count(data, hsc12_T3)
knitr::kable(hsc12_T3_count) #テーブル化
| 2 |
1 |
| 3 |
2 |
| 4 |
9 |
| 5 |
27 |
| 6 |
39 |
| 7 |
27 |
| NA |
9 |
nn <- ggplot(data = data, mapping = aes(x = hsc12_T3, fill = factor(hsc12_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(nn) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc1_T4の度数分布とヒストグラム
hsc1_T4_count <- dplyr::count(data, hsc1_T4)
knitr::kable(hsc1_T4_count) #テーブル化
| 1 |
1 |
| 2 |
2 |
| 3 |
15 |
| 4 |
17 |
| 5 |
41 |
| 6 |
27 |
| 7 |
3 |
| NA |
8 |
oo <- ggplot(data = data, mapping = aes(x = hsc1_T4, fill = factor(hsc1_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(oo) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc2_T4の度数分布とヒストグラム
hsc2_T4_count <- dplyr::count(data, hsc2_T4)
knitr::kable(hsc2_T4_count) #テーブル化
| 2 |
9 |
| 3 |
11 |
| 4 |
9 |
| 5 |
32 |
| 6 |
32 |
| 7 |
13 |
| NA |
8 |
pp <- ggplot(data = data, mapping = aes(x = hsc2_T4, fill = factor(hsc2_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(pp) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc3_T4の度数分布とヒストグラム
hsc3_T4_count <- dplyr::count(data, hsc3_T4)
knitr::kable(hsc3_T4_count) #テーブル化
| 2 |
2 |
| 3 |
2 |
| 4 |
9 |
| 5 |
21 |
| 6 |
29 |
| 7 |
43 |
| NA |
8 |
qq <- ggplot(data = data, mapping = aes(x = hsc3_T4, fill = factor(hsc3_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(qq) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc4_T4の度数分布とヒストグラム
hsc4_T4_count <- dplyr::count(data, hsc4_T4)
knitr::kable(hsc4_T4_count) #テーブル化
| 1 |
5 |
| 2 |
5 |
| 3 |
13 |
| 4 |
12 |
| 5 |
35 |
| 6 |
22 |
| 7 |
14 |
| NA |
8 |
rr <- ggplot(data = data, mapping = aes(x = hsc4_T4, fill = factor(hsc4_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(rr) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc5_T4の度数分布とヒストグラム
hsc5_T4_count <- dplyr::count(data, hsc5_T4)
knitr::kable(hsc5_T4_count) #テーブル化
| 2 |
1 |
| 3 |
4 |
| 4 |
3 |
| 5 |
19 |
| 6 |
23 |
| 7 |
54 |
| NA |
10 |
ss <- ggplot(data = data, mapping = aes(x = hsc5_T4, fill = factor(hsc5_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ss) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc6_T4の度数分布とヒストグラム
hsc6_T4_count <- dplyr::count(data, hsc6_T4)
knitr::kable(hsc6_T4_count) #テーブル化
| 1 |
2 |
| 2 |
8 |
| 3 |
8 |
| 4 |
9 |
| 5 |
38 |
| 6 |
29 |
| 7 |
12 |
| NA |
8 |
inu <- ggplot(data = data, mapping = aes(x = hsc6_T4, fill = factor(hsc6_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(inu) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc7_T4の度数分布とヒストグラム
hsc7_T4_count <- dplyr::count(data, hsc7_T4)
knitr::kable(hsc7_T4_count) #テーブル化
| 1 |
4 |
| 2 |
9 |
| 3 |
11 |
| 4 |
16 |
| 5 |
18 |
| 6 |
27 |
| 7 |
21 |
| NA |
8 |
tt <- ggplot(data = data, mapping = aes(x = hsc7_T4, fill = factor(hsc7_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(tt) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc8_T4の度数分布とヒストグラム
hsc8_T4_count <- dplyr::count(data, hsc8_T4)
knitr::kable(hsc8_T4_count) #テーブル化
| 1 |
2 |
| 2 |
5 |
| 3 |
17 |
| 4 |
20 |
| 5 |
34 |
| 6 |
17 |
| 7 |
11 |
| NA |
8 |
vv <- ggplot(data = data, mapping = aes(x = hsc8_T4, fill = factor(hsc8_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(vv) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc9_T4の度数分布とヒストグラム
hsc9_T4_count <- dplyr::count(data, hsc9_T4)
knitr::kable(hsc9_T4_count) #テーブル化
| 1 |
6 |
| 2 |
13 |
| 3 |
19 |
| 4 |
30 |
| 5 |
24 |
| 6 |
11 |
| 7 |
3 |
| NA |
8 |
ww <- ggplot(data = data, mapping = aes(x = hsc9_T4, fill = factor(hsc9_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ww) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc10_T4の度数分布とヒストグラム
hsc10_T4_count <- dplyr::count(data, hsc10_T4)
knitr::kable(hsc10_T4_count) #テーブル化
| 2 |
1 |
| 3 |
1 |
| 4 |
3 |
| 5 |
11 |
| 6 |
30 |
| 7 |
60 |
| NA |
8 |
kame <- ggplot(data = data, mapping = aes(x = hsc10_T4, fill = factor(hsc10_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(kame) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc11_T4の度数分布とヒストグラム
hsc11_T4_count <- dplyr::count(data, hsc11_T4)
knitr::kable(hsc11_T4_count) #テーブル化
| 1 |
2 |
| 2 |
10 |
| 3 |
11 |
| 4 |
18 |
| 5 |
28 |
| 6 |
22 |
| 7 |
15 |
| NA |
8 |
yy <- ggplot(data = data, mapping = aes(x = hsc11_T4, fill = factor(hsc11_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(yy) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc12_T4の度数分布とヒストグラム
hsc12_T4_count <- dplyr::count(data, hsc12_T4)
knitr::kable(hsc12_T4_count) #テーブル化
| 1 |
1 |
| 2 |
5 |
| 3 |
2 |
| 4 |
7 |
| 5 |
31 |
| 6 |
31 |
| 7 |
29 |
| NA |
8 |
zz <- ggplot(data = data, mapping = aes(x = hsc12_T4, fill = factor(hsc12_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(zz) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb1_T1の度数分布とヒストグラム
wb1_T1_count <- dplyr::count(data, wb1_T1)
knitr::kable(wb1_T1_count) #テーブル化
| 0 |
1 |
| 1 |
6 |
| 2 |
27 |
| 3 |
39 |
| 4 |
31 |
| 5 |
10 |
aaa <- ggplot(data = data, mapping = aes(x = wb1_T1, fill = factor(wb1_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(aaa) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb2_T1の度数分布とヒストグラム
wb2_T1_count <- dplyr::count(data, wb2_T1)
knitr::kable(wb2_T1_count) #テーブル化
| 0 |
2 |
| 1 |
12 |
| 2 |
31 |
| 3 |
40 |
| 4 |
23 |
| 5 |
6 |
bbb <- ggplot(data = data, mapping = aes(x = wb2_T1, fill = factor(wb2_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(bbb) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb3_T1の度数分布とヒストグラム
wb3_T1_count <- dplyr::count(data, wb3_T1)
knitr::kable(wb3_T1_count) #テーブル化
| 0 |
2 |
| 1 |
11 |
| 2 |
26 |
| 3 |
36 |
| 4 |
28 |
| 5 |
10 |
| NA |
1 |
ccc <- ggplot(data = data, mapping = aes(x = wb3_T1, fill = factor(wb3_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ccc) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb4_T1の度数分布とヒストグラム
wb4_T1_count <- dplyr::count(data, wb4_T1)
knitr::kable(wb4_T1_count) #テーブル化
| 0 |
4 |
| 1 |
35 |
| 2 |
34 |
| 3 |
27 |
| 4 |
9 |
| 5 |
5 |
ddd <- ggplot(data = data, mapping = aes(x = wb4_T1, fill = factor(wb4_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ddd) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb5_T1の度数分布とヒストグラム
wb5_T1_count <- dplyr::count(data, wb5_T1)
knitr::kable(wb5_T1_count) #テーブル化
| 0 |
3 |
| 1 |
15 |
| 2 |
29 |
| 3 |
38 |
| 4 |
22 |
| 5 |
7 |
eee <- ggplot(data = data, mapping = aes(x = wb5_T1, fill = factor(wb5_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(eee) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb1_T2の度数分布とヒストグラム
wb1_T2_count <- dplyr::count(data, wb1_T2)
knitr::kable(wb1_T2_count) #テーブル化
| 1 |
5 |
| 2 |
23 |
| 3 |
30 |
| 4 |
33 |
| 5 |
9 |
| NA |
14 |
fff <- ggplot(data = data, mapping = aes(x = wb1_T2, fill = factor(wb1_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(fff) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb2_T2の度数分布とヒストグラム
wb2_T2_count <- dplyr::count(data, wb2_T2)
knitr::kable(wb2_T2_count) #テーブル化
| 1 |
7 |
| 2 |
23 |
| 3 |
28 |
| 4 |
32 |
| 5 |
9 |
| NA |
15 |
ggg <- ggplot(data = data, mapping = aes(x = wb2_T2, fill = factor(wb2_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ggg) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb3_T2の度数分布とヒストグラム
wb3_T2_count <- dplyr::count(data, wb3_T2)
knitr::kable(wb3_T2_count) #テーブル化
| 1 |
10 |
| 2 |
16 |
| 3 |
36 |
| 4 |
25 |
| 5 |
13 |
| NA |
14 |
hhh <- ggplot(data = data, mapping = aes(x = wb3_T2, fill = factor(wb3_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(hhh) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb4_T2の度数分布とヒストグラム
wb4_T2_count <- dplyr::count(data, wb4_T2)
knitr::kable(wb4_T2_count) #テーブル化
| 0 |
2 |
| 1 |
26 |
| 2 |
29 |
| 3 |
23 |
| 4 |
12 |
| 5 |
8 |
| NA |
14 |
iii <- ggplot(data = data, mapping = aes(x = wb4_T2, fill = factor(wb4_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(iii) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb5_T2の度数分布とヒストグラム
wb5_T2_count <- dplyr::count(data, wb5_T2)
knitr::kable(wb5_T2_count) #テーブル化
| 1 |
12 |
| 2 |
26 |
| 3 |
28 |
| 4 |
21 |
| 5 |
13 |
| NA |
14 |
jjj <- ggplot(data = data, mapping = aes(x = wb5_T2, fill = factor(wb5_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(jjj) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb1_T3の度数分布とヒストグラム
wb1_T3_count <- dplyr::count(data, wb1_T3)
knitr::kable(wb1_T3_count) #テーブル化
| 0 |
1 |
| 1 |
6 |
| 2 |
18 |
| 3 |
36 |
| 4 |
28 |
| 5 |
16 |
| NA |
9 |
kkk <- ggplot(data = data, mapping = aes(x = wb1_T3, fill = factor(wb1_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(kkk) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb2_T3の度数分布とヒストグラム
wb2_T3_count <- dplyr::count(data, wb2_T3)
knitr::kable(wb2_T3_count) #テーブル化
| 0 |
1 |
| 1 |
12 |
| 2 |
22 |
| 3 |
32 |
| 4 |
27 |
| 5 |
11 |
| NA |
9 |
lll <- ggplot(data = data, mapping = aes(x = wb2_T3, fill = factor(wb2_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(lll) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb3_T3の度数分布とヒストグラム
wb3_T3_count <- dplyr::count(data, wb3_T3)
knitr::kable(wb1_T3_count) #テーブル化
| 0 |
1 |
| 1 |
6 |
| 2 |
18 |
| 3 |
36 |
| 4 |
28 |
| 5 |
16 |
| NA |
9 |
mmm <- ggplot(data = data, mapping = aes(x = wb3_T3, fill = factor(wb3_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(mmm) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb4_T3の度数分布とヒストグラム
wb4_T3_count <- dplyr::count(data, wb4_T3)
knitr::kable(wb4_T3_count) #テーブル化
| 0 |
6 |
| 1 |
20 |
| 2 |
40 |
| 3 |
18 |
| 4 |
15 |
| 5 |
6 |
| NA |
9 |
nnn <- ggplot(data = data, mapping = aes(x = wb4_T3, fill = factor(wb4_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(nnn) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb5_T3の度数分布とヒストグラム
wb5_T3_count <- dplyr::count(data, wb5_T3)
knitr::kable(wb5_T3_count) #テーブル化
| 0 |
1 |
| 1 |
16 |
| 2 |
27 |
| 3 |
38 |
| 4 |
16 |
| 5 |
7 |
| NA |
9 |
ooo <- ggplot(data = data, mapping = aes(x = wb5_T3, fill = factor(wb5_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ooo) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb1_T4の度数分布とヒストグラム
wb1_T4_count <- dplyr::count(data, wb1_T4)
knitr::kable(wb1_T4_count) #テーブル化
| 0 |
1 |
| 1 |
10 |
| 2 |
17 |
| 3 |
34 |
| 4 |
34 |
| 5 |
10 |
| NA |
8 |
ppp <- ggplot(data = data, mapping = aes(x = wb1_T4, fill = factor(wb1_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ppp) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb2_T4の度数分布とヒストグラム
wb2_T4_count <- dplyr::count(data, wb2_T4)
knitr::kable(wb2_T4_count) #テーブル化
| 0 |
3 |
| 1 |
14 |
| 2 |
24 |
| 3 |
32 |
| 4 |
21 |
| 5 |
12 |
| NA |
8 |
qqq <- ggplot(data = data, mapping = aes(x = wb2_T4, fill = factor(wb2_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(qqq) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb3_T4の度数分布とヒストグラム
wb3_T4_count <- dplyr::count(data, wb3_T4)
knitr::kable(wb3_T4_count) #テーブル化
| 0 |
1 |
| 1 |
9 |
| 2 |
25 |
| 3 |
31 |
| 4 |
23 |
| 5 |
17 |
| NA |
8 |
rrr <- ggplot(data = data, mapping = aes(x = wb3_T4, fill = factor(wb3_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(rrr) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb5_T4の度数分布とヒストグラム
wb5_T4_count <- dplyr::count(data, wb5_T4)
knitr::kable(wb5_T4_count) #テーブル化
| 0 |
4 |
| 1 |
12 |
| 2 |
25 |
| 3 |
30 |
| 4 |
20 |
| 5 |
15 |
| NA |
8 |
sss <- ggplot(data = data, mapping = aes(x = wb5_T4, fill = factor(wb5_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(sss) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#event1_T1の度数分布とヒストグラム
event1_T1_count <- dplyr::count(data, ev1_T1)
knitr::kable(event1_T1_count) #テーブル化
| -3 |
16 |
| -2 |
11 |
| -1 |
9 |
| 0 |
3 |
| 1 |
8 |
| 2 |
11 |
| 3 |
55 |
| NA |
1 |
ttt <- ggplot(data = data, mapping = aes(x = ev1_T1, fill = factor(ev1_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ttt) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#event2_T1の度数分布とヒストグラム
event2_T1_count <- dplyr::count(data, ev2_T1)
knitr::kable(event2_T1_count) #テーブル化
| -3 |
21 |
| -2 |
16 |
| -1 |
8 |
| 0 |
5 |
| 1 |
2 |
| 2 |
19 |
| 3 |
42 |
| NA |
1 |
uuu <- ggplot(data = data, mapping = aes(x = ev2_T1, fill = factor(ev2_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(uuu) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#event1_T2の度数分布とヒストグラム
event1_T2_count <- dplyr::count(data, ev1_T2)
knitr::kable(event1_T2_count) #テーブル化
| -3 |
17 |
| -2 |
7 |
| -1 |
4 |
| 0 |
4 |
| 1 |
6 |
| 2 |
13 |
| 3 |
47 |
| NA |
16 |
vvv <- ggplot(data = data, mapping = aes(x = ev1_T2, fill = factor(ev1_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(vvv) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#event2_T2の度数分布とヒストグラム
event2_T2_count <- dplyr::count(data, ev2_T2)
knitr::kable(event2_T2_count) #テーブル化
| -3 |
20 |
| -2 |
15 |
| -1 |
5 |
| 0 |
1 |
| 1 |
8 |
| 2 |
17 |
| 3 |
29 |
| NA |
19 |
www <- ggplot(data = data, mapping = aes(x = ev2_T2, fill = factor(ev2_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(www) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#event1_T3の度数分布とヒストグラム
event1_T3_count <- dplyr::count(data, ev1_T3)
knitr::kable(event1_T3_count) #テーブル化
| -3 |
19 |
| -2 |
6 |
| -1 |
10 |
| 0 |
6 |
| 1 |
4 |
| 2 |
11 |
| 3 |
47 |
| NA |
11 |
usagi <- ggplot(data = data, mapping = aes(x = ev1_T3, fill = factor(ev1_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(usagi) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#event2_T3の度数分布とヒストグラム
event2_T3_count <- dplyr::count(data, ev2_T3)
knitr::kable(event2_T3_count) #テーブル化
| -3 |
22 |
| -2 |
12 |
| -1 |
4 |
| 0 |
5 |
| 1 |
13 |
| 2 |
13 |
| 3 |
34 |
| NA |
11 |
yyy <- ggplot(data = data, mapping = aes(x = ev2_T3, fill = factor(ev2_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(yyy) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#event1_T4の度数分布とヒストグラム
event1_T4_count <- dplyr::count(data, ev1_T4)
knitr::kable(event1_T4_count) #テーブル化
| -3 |
15 |
| -2 |
5 |
| -1 |
3 |
| 0 |
4 |
| 1 |
10 |
| 2 |
15 |
| 3 |
49 |
| NA |
13 |
zzz <- ggplot(data = data, mapping = aes(x = ev1_T4, fill = factor(ev1_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(zzz) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#event2_T4の度数分布とヒストグラム
event2_T4_count <- dplyr::count(data, ev2_T4)
knitr::kable(event2_T4_count) #テーブル化
| -3 |
15 |
| -2 |
13 |
| -1 |
10 |
| 0 |
9 |
| 1 |
9 |
| 2 |
14 |
| 3 |
31 |
| NA |
13 |
aaaa <- ggplot(data = data, mapping = aes(x = ev2_T4, fill = factor(ev2_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(aaaa) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#eoe_T1の度数分布とヒストグラム
eoe_T1_count <- dplyr::count(data, eoe_T1)
knitr::kable(eoe_T1_count) #テーブル化
| 1.8 |
2 |
| 2.2 |
1 |
| 2.4 |
2 |
| 2.6 |
1 |
| 2.8 |
1 |
| 3.0 |
1 |
| 3.2 |
3 |
| 3.4 |
5 |
| 3.6 |
2 |
| 3.8 |
10 |
| 4.0 |
6 |
| 4.2 |
14 |
| 4.4 |
6 |
| 4.6 |
7 |
| 4.8 |
11 |
| 5.0 |
9 |
| 5.2 |
10 |
| 5.4 |
5 |
| 5.6 |
6 |
| 5.8 |
5 |
| 6.0 |
4 |
| 6.4 |
3 |
bbbb <- ggplot(data = data, mapping = aes(x = eoe_T1, fill = factor(eoe_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(bbbb) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#eoe_T2の度数分布とヒストグラム
eoe_T2_count <- dplyr::count(data, eoe_T2)
knitr::kable(eoe_T2_count) #テーブル化
| 1.8 |
2 |
| 2.0 |
1 |
| 2.4 |
1 |
| 2.6 |
2 |
| 2.8 |
2 |
| 3.0 |
1 |
| 3.2 |
2 |
| 3.4 |
2 |
| 3.6 |
2 |
| 3.8 |
4 |
| 4.0 |
5 |
| 4.2 |
8 |
| 4.4 |
6 |
| 4.6 |
9 |
| 4.8 |
16 |
| 5.0 |
3 |
| 5.2 |
7 |
| 5.4 |
5 |
| 5.6 |
9 |
| 5.8 |
2 |
| 6.0 |
3 |
| 6.2 |
4 |
| 6.4 |
1 |
| 6.6 |
1 |
| 6.8 |
1 |
| NA |
15 |
cccc <- ggplot(data = data, mapping = aes(x = eoe_T2, fill = factor(eoe_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(cccc) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#eoe_T3の度数分布とヒストグラム
eoe_T3_count <- dplyr::count(data, eoe_T3)
knitr::kable(eoe_T3_count) #テーブル化
| 2.0 |
1 |
| 2.2 |
1 |
| 2.4 |
1 |
| 2.6 |
1 |
| 3.0 |
2 |
| 3.2 |
1 |
| 3.6 |
1 |
| 3.8 |
4 |
| 4.0 |
3 |
| 4.2 |
5 |
| 4.4 |
7 |
| 4.6 |
10 |
| 4.8 |
8 |
| 5.0 |
14 |
| 5.2 |
11 |
| 5.4 |
11 |
| 5.6 |
7 |
| 5.8 |
3 |
| 6.0 |
2 |
| 6.2 |
2 |
| 6.4 |
5 |
| 6.6 |
2 |
| 7.0 |
2 |
| NA |
10 |
dddd <- ggplot(data = data, mapping = aes(x = eoe_T3, fill = factor(eoe_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(dddd) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#eoe_T4の度数分布とヒストグラム
eoe_T4_count <- dplyr::count(data, eoe_T4)
knitr::kable(eoe_T4_count) #テーブル化
| 1.0 |
1 |
| 2.0 |
1 |
| 2.2 |
1 |
| 2.4 |
1 |
| 2.6 |
1 |
| 2.8 |
3 |
| 3.0 |
2 |
| 3.2 |
3 |
| 3.4 |
1 |
| 3.6 |
1 |
| 3.8 |
3 |
| 4.0 |
6 |
| 4.2 |
2 |
| 4.4 |
7 |
| 4.6 |
13 |
| 4.8 |
7 |
| 5.0 |
7 |
| 5.2 |
10 |
| 5.4 |
7 |
| 5.6 |
11 |
| 5.8 |
6 |
| 6.0 |
5 |
| 6.2 |
2 |
| 6.4 |
2 |
| 6.6 |
1 |
| 6.8 |
1 |
| 7.0 |
1 |
| NA |
8 |
eeee <- ggplot(data = data, mapping = aes(x = eoe_T4, fill = factor(eoe_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(eeee) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#lst_T1の度数分布とヒストグラム
lst_T1_count <- dplyr::count(data, lst_T1)
knitr::kable(lst_T1_count) #テーブル化
| 1.5 |
2 |
| 2.0 |
3 |
| 2.5 |
3 |
| 3.0 |
7 |
| 3.5 |
15 |
| 4.0 |
8 |
| 4.5 |
14 |
| 5.0 |
15 |
| 5.5 |
16 |
| 6.0 |
10 |
| 6.5 |
9 |
| 7.0 |
12 |
ffff <- ggplot(data = data, mapping = aes(x = lst_T1, fill = factor(lst_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ffff) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#lst_T2の度数分布とヒストグラム
lst_T2_count <- dplyr::count(data, lst_T2)
knitr::kable(lst_T2_count) #テーブル化
| 1.0 |
1 |
| 2.0 |
6 |
| 2.5 |
4 |
| 3.0 |
5 |
| 3.5 |
6 |
| 4.0 |
9 |
| 4.5 |
11 |
| 5.0 |
18 |
| 5.5 |
13 |
| 6.0 |
12 |
| 6.5 |
7 |
| 7.0 |
7 |
| NA |
15 |
gggg <- ggplot(data = data, mapping = aes(x = lst_T2, fill = factor(lst_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(gggg) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#lst_T3の度数分布とヒストグラム
lst_T3_count <- dplyr::count(data, lst_T3)
knitr::kable(lst_T3_count) #テーブル化
| 1.0 |
1 |
| 1.5 |
1 |
| 2.0 |
2 |
| 2.5 |
1 |
| 3.0 |
8 |
| 3.5 |
5 |
| 4.0 |
13 |
| 4.5 |
7 |
| 5.0 |
17 |
| 5.5 |
13 |
| 6.0 |
19 |
| 6.5 |
7 |
| 7.0 |
11 |
| NA |
9 |
hhhh <- ggplot(data = data, mapping = aes(x = lst_T3, fill = factor(lst_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(hhhh) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#lst_T4の度数分布とヒストグラム
lst_T4_count <- dplyr::count(data, lst_T4)
knitr::kable(lst_T4_count) #テーブル化
| 2.0 |
6 |
| 2.5 |
3 |
| 3.0 |
9 |
| 3.5 |
4 |
| 4.0 |
10 |
| 4.5 |
8 |
| 5.0 |
22 |
| 5.5 |
11 |
| 6.0 |
18 |
| 6.5 |
6 |
| 7.0 |
9 |
| NA |
8 |
iiii <- ggplot(data = data, mapping = aes(x = lst_T4, fill = factor(lst_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(iiii) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#aes_T1の度数分布とヒストグラム
aes_T1_count <- dplyr::count(data, aes_T1)
knitr::kable(aes_T1_count) #テーブル化
| 3.25 |
1 |
| 4.25 |
1 |
| 4.50 |
2 |
| 4.75 |
6 |
| 5.00 |
6 |
| 5.25 |
15 |
| 5.50 |
9 |
| 5.75 |
22 |
| 6.00 |
21 |
| 6.25 |
11 |
| 6.50 |
9 |
| 6.75 |
5 |
| 7.00 |
5 |
| NA |
1 |
jjjj <- ggplot(data = data, mapping = aes(x = aes_T1, fill = factor(aes_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(jjjj) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#aes_T2の度数分布とヒストグラム
aes_T2_count <- dplyr::count(data, aes_T2)
knitr::kable(aes_T2_count) #テーブル化
| 2.50 |
1 |
| 3.50 |
1 |
| 4.00 |
1 |
| 4.50 |
3 |
| 4.75 |
3 |
| 5.00 |
10 |
| 5.25 |
12 |
| 5.50 |
15 |
| 5.75 |
11 |
| 6.00 |
13 |
| 6.25 |
15 |
| 6.50 |
6 |
| 6.75 |
6 |
| 7.00 |
3 |
| NA |
14 |
kkkk <- ggplot(data = data, mapping = aes(x = aes_T2, fill = factor(aes_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(kkkk) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#aes_T3の度数分布とヒストグラム
aes_T3_count <- dplyr::count(data, aes_T3)
knitr::kable(aes_T3_count) #テーブル化
| 4.00 |
2 |
| 4.25 |
1 |
| 4.50 |
2 |
| 4.75 |
1 |
| 5.00 |
10 |
| 5.25 |
9 |
| 5.50 |
13 |
| 5.75 |
15 |
| 6.00 |
9 |
| 6.25 |
17 |
| 6.50 |
14 |
| 6.75 |
7 |
| 7.00 |
5 |
| NA |
9 |
llll <- ggplot(data = data, mapping = aes(x = aes_T3, fill = factor(aes_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(llll) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#aes_T4の度数分布とヒストグラム
aes_T4_count <- dplyr::count(data, aes_T4)
knitr::kable(aes_T4_count) #テーブル化
| 2.75 |
2 |
| 3.50 |
1 |
| 4.25 |
2 |
| 4.50 |
2 |
| 4.75 |
4 |
| 5.00 |
5 |
| 5.25 |
10 |
| 5.50 |
12 |
| 5.75 |
10 |
| 6.00 |
16 |
| 6.25 |
13 |
| 6.50 |
12 |
| 6.75 |
14 |
| 7.00 |
1 |
| NA |
10 |
mmmm <- ggplot(data = data, mapping = aes(x = aes_T4, fill = factor(aes_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(mmmm) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc_T1の度数分布とヒストグラム
hsc_T1_count <- dplyr::count(data, hsc_T1)
knitr::kable(hsc_T1_count) #テーブル化
| 3.183333 |
1 |
| 3.300000 |
1 |
| 3.383333 |
1 |
| 3.400000 |
1 |
| 3.416667 |
1 |
| 3.650000 |
1 |
| 3.766667 |
1 |
| 3.933333 |
1 |
| 3.966667 |
1 |
| 3.983333 |
1 |
| 4.016667 |
1 |
| 4.066667 |
1 |
| 4.116667 |
1 |
| 4.150000 |
1 |
| 4.183333 |
1 |
| 4.266667 |
1 |
| 4.283333 |
1 |
| 4.316667 |
1 |
| 4.350000 |
2 |
| 4.383333 |
1 |
| 4.433333 |
1 |
| 4.450000 |
1 |
| 4.483333 |
3 |
| 4.500000 |
2 |
| 4.516667 |
1 |
| 4.633333 |
1 |
| 4.650000 |
1 |
| 4.683333 |
1 |
| 4.733333 |
1 |
| 4.750000 |
2 |
| 4.766667 |
2 |
| 4.800000 |
1 |
| 4.816667 |
1 |
| 4.850000 |
5 |
| 4.866667 |
1 |
| 4.883333 |
1 |
| 4.916667 |
1 |
| 4.933333 |
1 |
| 5.000000 |
2 |
| 5.016667 |
1 |
| 5.050000 |
1 |
| 5.066667 |
1 |
| 5.116667 |
1 |
| 5.150000 |
1 |
| 5.166667 |
2 |
| 5.200000 |
1 |
| 5.216667 |
2 |
| 5.233333 |
1 |
| 5.250000 |
1 |
| 5.283333 |
2 |
| 5.300000 |
1 |
| 5.316667 |
3 |
| 5.333333 |
2 |
| 5.366667 |
1 |
| 5.383333 |
1 |
| 5.416667 |
1 |
| 5.433333 |
2 |
| 5.450000 |
1 |
| 5.466667 |
1 |
| 5.483333 |
3 |
| 5.500000 |
1 |
| 5.516667 |
2 |
| 5.533333 |
1 |
| 5.566667 |
3 |
| 5.583333 |
1 |
| 5.616667 |
3 |
| 5.666667 |
1 |
| 5.683333 |
1 |
| 5.700000 |
1 |
| 5.733333 |
2 |
| 5.766667 |
1 |
| 5.800000 |
4 |
| 5.833333 |
1 |
| 5.850000 |
1 |
| 5.900000 |
1 |
| 5.916667 |
1 |
| 6.066667 |
1 |
| 6.083333 |
1 |
| 6.116667 |
1 |
| 6.133333 |
1 |
| 6.300000 |
1 |
| 6.383333 |
1 |
| 6.466667 |
1 |
| 6.600000 |
1 |
| NA |
1 |
nnnn <- ggplot(data = data, mapping = aes(x = hsc_T1, fill = factor(hsc_T1))) + geom_histogram(binwidth = 0.1) #視覚化
ggplotly(nnnn) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc_T2の度数分布とヒストグラム
hsc_T2_count <- dplyr::count(data, hsc_T2)
knitr::kable(hsc_T2_count) #テーブル化
| 2.533333 |
1 |
| 2.916667 |
1 |
| 3.066667 |
1 |
| 3.600000 |
1 |
| 3.616667 |
1 |
| 3.716667 |
1 |
| 3.850000 |
1 |
| 4.000000 |
1 |
| 4.016667 |
1 |
| 4.033333 |
1 |
| 4.050000 |
1 |
| 4.100000 |
2 |
| 4.166667 |
2 |
| 4.233333 |
1 |
| 4.316667 |
1 |
| 4.400000 |
1 |
| 4.433333 |
1 |
| 4.466667 |
1 |
| 4.500000 |
1 |
| 4.516667 |
3 |
| 4.533333 |
1 |
| 4.550000 |
2 |
| 4.566667 |
1 |
| 4.633333 |
1 |
| 4.650000 |
1 |
| 4.733333 |
2 |
| 4.766667 |
1 |
| 4.783333 |
2 |
| 4.866667 |
1 |
| 4.883333 |
2 |
| 4.900000 |
1 |
| 4.933333 |
2 |
| 4.950000 |
1 |
| 4.983333 |
1 |
| 5.000000 |
1 |
| 5.016667 |
1 |
| 5.066667 |
1 |
| 5.100000 |
1 |
| 5.116667 |
1 |
| 5.150000 |
2 |
| 5.183333 |
2 |
| 5.200000 |
2 |
| 5.233333 |
2 |
| 5.250000 |
1 |
| 5.283333 |
1 |
| 5.300000 |
1 |
| 5.333333 |
1 |
| 5.350000 |
1 |
| 5.383333 |
1 |
| 5.416667 |
2 |
| 5.433333 |
2 |
| 5.450000 |
1 |
| 5.466667 |
1 |
| 5.516667 |
1 |
| 5.533333 |
1 |
| 5.616667 |
2 |
| 5.633333 |
1 |
| 5.666667 |
2 |
| 5.683333 |
3 |
| 5.700000 |
3 |
| 5.766667 |
1 |
| 5.783333 |
1 |
| 5.800000 |
1 |
| 5.816667 |
1 |
| 5.866667 |
1 |
| 5.883333 |
1 |
| 5.950000 |
1 |
| 6.016667 |
2 |
| 6.066667 |
2 |
| 6.150000 |
1 |
| 6.233333 |
2 |
| 6.283333 |
1 |
| 6.683333 |
1 |
| 6.800000 |
1 |
| NA |
16 |
oooo <- ggplot(data = data, mapping = aes(x = hsc_T2, fill = factor(hsc_T2))) + geom_histogram(binwidth = 0.1) #視覚化
ggplotly(oooo) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc_T3の度数分布とヒストグラム
hsc_T3_count <- dplyr::count(data, hsc_T3)
knitr::kable(hsc_T3_count) #テーブル化
| 3.383333 |
1 |
| 3.483333 |
1 |
| 3.500000 |
1 |
| 3.766667 |
1 |
| 3.916667 |
1 |
| 3.950000 |
1 |
| 4.000000 |
1 |
| 4.016667 |
1 |
| 4.083333 |
1 |
| 4.150000 |
1 |
| 4.216667 |
1 |
| 4.283333 |
1 |
| 4.400000 |
1 |
| 4.433333 |
1 |
| 4.450000 |
1 |
| 4.483333 |
1 |
| 4.533333 |
1 |
| 4.566667 |
2 |
| 4.700000 |
2 |
| 4.766667 |
2 |
| 4.800000 |
1 |
| 4.866667 |
2 |
| 4.883333 |
1 |
| 4.916667 |
2 |
| 4.933333 |
1 |
| 4.950000 |
1 |
| 4.966667 |
2 |
| 4.983333 |
2 |
| 5.000000 |
1 |
| 5.033333 |
2 |
| 5.050000 |
1 |
| 5.100000 |
2 |
| 5.116667 |
1 |
| 5.133333 |
4 |
| 5.166667 |
2 |
| 5.183333 |
1 |
| 5.233333 |
1 |
| 5.250000 |
1 |
| 5.300000 |
1 |
| 5.316667 |
1 |
| 5.333333 |
2 |
| 5.400000 |
2 |
| 5.416667 |
4 |
| 5.433333 |
1 |
| 5.483333 |
2 |
| 5.500000 |
1 |
| 5.516667 |
1 |
| 5.533333 |
1 |
| 5.550000 |
1 |
| 5.583333 |
1 |
| 5.616667 |
1 |
| 5.633333 |
1 |
| 5.666667 |
1 |
| 5.683333 |
2 |
| 5.700000 |
1 |
| 5.716667 |
1 |
| 5.733333 |
2 |
| 5.800000 |
2 |
| 5.816667 |
1 |
| 5.883333 |
1 |
| 5.950000 |
1 |
| 5.983333 |
1 |
| 6.000000 |
1 |
| 6.016667 |
1 |
| 6.033333 |
1 |
| 6.050000 |
1 |
| 6.083333 |
1 |
| 6.100000 |
1 |
| 6.116667 |
1 |
| 6.133333 |
2 |
| 6.150000 |
1 |
| 6.166667 |
1 |
| 6.216667 |
1 |
| 6.333333 |
1 |
| 6.366667 |
1 |
| 6.383333 |
1 |
| 6.450000 |
2 |
| 6.566667 |
1 |
| 6.800000 |
1 |
| 6.916667 |
1 |
| NA |
10 |
pppp <- ggplot(data = data, mapping = aes(x = hsc_T3, fill = factor(hsc_T3))) + geom_histogram(binwidth = 0.1) #視覚化
ggplotly(pppp) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc_T4の度数分布とヒストグラム
hsc_T4_count <- dplyr::count(data, hsc_T4)
knitr::kable(hsc_T4_count) #テーブル化
| 2.616667 |
1 |
| 3.083333 |
1 |
| 3.183333 |
1 |
| 3.583333 |
1 |
| 3.633333 |
1 |
| 3.833333 |
1 |
| 3.850000 |
1 |
| 3.916667 |
1 |
| 3.933333 |
2 |
| 4.066667 |
1 |
| 4.233333 |
2 |
| 4.250000 |
1 |
| 4.333333 |
2 |
| 4.350000 |
1 |
| 4.366667 |
1 |
| 4.516667 |
1 |
| 4.533333 |
1 |
| 4.616667 |
3 |
| 4.633333 |
2 |
| 4.650000 |
1 |
| 4.666667 |
1 |
| 4.716667 |
1 |
| 4.750000 |
1 |
| 4.766667 |
1 |
| 4.783333 |
3 |
| 4.800000 |
1 |
| 4.833333 |
2 |
| 4.850000 |
1 |
| 4.866667 |
2 |
| 4.883333 |
1 |
| 4.900000 |
1 |
| 4.916667 |
1 |
| 4.933333 |
1 |
| 4.950000 |
1 |
| 5.000000 |
1 |
| 5.033333 |
1 |
| 5.100000 |
1 |
| 5.116667 |
2 |
| 5.150000 |
1 |
| 5.166667 |
1 |
| 5.200000 |
1 |
| 5.250000 |
1 |
| 5.300000 |
2 |
| 5.316667 |
2 |
| 5.333333 |
1 |
| 5.350000 |
1 |
| 5.366667 |
1 |
| 5.383333 |
1 |
| 5.400000 |
1 |
| 5.416667 |
1 |
| 5.433333 |
1 |
| 5.466667 |
1 |
| 5.483333 |
1 |
| 5.533333 |
2 |
| 5.550000 |
2 |
| 5.566667 |
1 |
| 5.633333 |
1 |
| 5.683333 |
2 |
| 5.700000 |
1 |
| 5.716667 |
1 |
| 5.733333 |
1 |
| 5.750000 |
2 |
| 5.766667 |
3 |
| 5.783333 |
2 |
| 5.816667 |
1 |
| 5.850000 |
1 |
| 5.866667 |
1 |
| 5.883333 |
1 |
| 5.900000 |
2 |
| 5.966667 |
1 |
| 5.983333 |
1 |
| 6.050000 |
1 |
| 6.116667 |
1 |
| 6.183333 |
1 |
| 6.250000 |
2 |
| 6.283333 |
1 |
| 6.416667 |
1 |
| 6.433333 |
1 |
| 6.450000 |
1 |
| 6.483333 |
1 |
| 6.550000 |
1 |
| 7.000000 |
1 |
| NA |
10 |
qqqq <- ggplot(data = data, mapping = aes(x = hsc_T4, fill = factor(hsc_T4))) + geom_histogram(binwidth = 0.1) #視覚化
ggplotly(qqqq) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb_T1の度数分布とヒストグラム
wb_T1_count <- dplyr::count(data, wb_T1)
knitr::kable(wb_T1_count) #テーブル化
| 0.6 |
1 |
| 0.8 |
1 |
| 1.2 |
1 |
| 1.4 |
5 |
| 1.6 |
5 |
| 1.8 |
6 |
| 2.0 |
7 |
| 2.2 |
11 |
| 2.4 |
5 |
| 2.6 |
11 |
| 2.8 |
11 |
| 3.0 |
14 |
| 3.2 |
7 |
| 3.4 |
7 |
| 3.6 |
7 |
| 3.8 |
5 |
| 4.0 |
3 |
| 4.2 |
3 |
| 4.4 |
1 |
| 4.6 |
1 |
| 5.0 |
1 |
| NA |
1 |
rrrr <- ggplot(data = data, mapping = aes(x = wb_T1, fill = factor(wb_T1))) + geom_histogram(binwidth = 0.5) #視覚化
ggplotly(rrrr) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb_T2の度数分布とヒストグラム
wb_T2_count <- dplyr::count(data, wb_T2)
knitr::kable(wb_T2_count) #テーブル化
| 1.0 |
1 |
| 1.2 |
1 |
| 1.4 |
1 |
| 1.6 |
5 |
| 1.8 |
6 |
| 2.0 |
4 |
| 2.2 |
7 |
| 2.4 |
7 |
| 2.6 |
8 |
| 2.8 |
2 |
| 3.0 |
15 |
| 3.2 |
10 |
| 3.4 |
7 |
| 3.6 |
2 |
| 3.8 |
7 |
| 4.0 |
4 |
| 4.2 |
2 |
| 4.4 |
5 |
| 4.6 |
2 |
| 5.0 |
3 |
| NA |
15 |
ssss <- ggplot(data = data, mapping = aes(x = wb_T2, fill = factor(wb_T2))) + geom_histogram(binwidth = 0.5) #視覚化
ggplotly(ssss) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb_T3の度数分布とヒストグラム
wb_T3_count <- dplyr::count(data, wb_T3)
knitr::kable(wb_T3_count) #テーブル化
| 0.0 |
1 |
| 0.8 |
1 |
| 1.0 |
2 |
| 1.4 |
3 |
| 1.6 |
1 |
| 1.8 |
5 |
| 2.0 |
5 |
| 2.2 |
8 |
| 2.4 |
10 |
| 2.6 |
7 |
| 2.8 |
10 |
| 3.0 |
12 |
| 3.2 |
8 |
| 3.4 |
5 |
| 3.6 |
4 |
| 3.8 |
10 |
| 4.0 |
5 |
| 4.4 |
1 |
| 4.6 |
3 |
| 4.8 |
1 |
| 5.0 |
3 |
| NA |
9 |
tttt <- ggplot(data = data, mapping = aes(x = wb_T3, fill = factor(wb_T3))) + geom_histogram(binwidth = 0.5) #視覚化
ggplotly(tttt) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb_T4の度数分布とヒストグラム
wb_T4_count <- dplyr::count(data, wb_T4)
knitr::kable(wb_T4_count) #テーブル化
| 0.2 |
1 |
| 0.6 |
1 |
| 0.8 |
2 |
| 1.0 |
2 |
| 1.2 |
3 |
| 1.4 |
1 |
| 1.6 |
2 |
| 1.8 |
4 |
| 2.0 |
8 |
| 2.2 |
8 |
| 2.4 |
6 |
| 2.6 |
13 |
| 2.8 |
8 |
| 3.0 |
3 |
| 3.2 |
10 |
| 3.4 |
7 |
| 3.6 |
4 |
| 3.8 |
3 |
| 4.0 |
5 |
| 4.2 |
5 |
| 4.4 |
2 |
| 4.6 |
2 |
| 5.0 |
6 |
| NA |
8 |
uuuu <- ggplot(data = data, mapping = aes(x = wb_T4, fill = factor(wb_T4))) + geom_histogram(binwidth = 0.5) #視覚化
ggplotly(uuuu) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#ev_T1の度数分布とヒストグラム
ev_T1_count <- dplyr::count(data, ev_T1)
knitr::kable(ev_T1_count) #テーブル化
| -3.0 |
3 |
| -2.5 |
1 |
| -2.0 |
1 |
| -1.5 |
7 |
| -1.0 |
4 |
| -0.5 |
9 |
| 0.0 |
21 |
| 0.5 |
18 |
| 1.0 |
11 |
| 1.5 |
1 |
| 2.0 |
3 |
| 2.5 |
15 |
| 3.0 |
19 |
| NA |
1 |
vvvv <- ggplot(data = data, mapping = aes(x = ev_T1, fill = factor(ev_T1))) + geom_histogram(binwidth = 0.5) #視覚化
ggplotly(vvvv) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#ev_T2の度数分布とヒストグラム
ev_T2_count <- dplyr::count(data, ev_T2)
knitr::kable(ev_T2_count) #テーブル化
| -3.0 |
3 |
| -2.5 |
5 |
| -2.0 |
1 |
| -1.5 |
3 |
| -1.0 |
4 |
| -0.5 |
3 |
| 0.0 |
22 |
| 0.5 |
10 |
| 1.0 |
9 |
| 1.5 |
3 |
| 2.0 |
6 |
| 2.5 |
12 |
| 3.0 |
14 |
| NA |
19 |
wwww <- ggplot(data = data, mapping = aes(x = ev_T2, fill = factor(ev_T2))) + geom_histogram(binwidth = 0.5) #視覚化
ggplotly(wwww) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#ev_T3の度数分布とヒストグラム
ev_T3_count <- dplyr::count(data, ev_T3)
knitr::kable(ev_T3_count) #テーブル化
| -3.0 |
7 |
| -2.0 |
3 |
| -1.5 |
1 |
| -1.0 |
3 |
| -0.5 |
8 |
| 0.0 |
20 |
| 0.5 |
17 |
| 1.0 |
8 |
| 1.5 |
6 |
| 2.0 |
10 |
| 2.5 |
5 |
| 3.0 |
15 |
| NA |
11 |
panda <- ggplot(data = data, mapping = aes(x = ev_T3, fill = factor(ev_T3))) + geom_histogram(binwidth = 0.5) #視覚化
ggplotly(panda) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#ev_T4の度数分布とヒストグラム
ev_T4_count <- dplyr::count(data, ev_T4)
knitr::kable(ev_T4_count) #テーブル化
| -3.0 |
3 |
| -2.0 |
1 |
| -1.0 |
3 |
| -0.5 |
10 |
| 0.0 |
21 |
| 0.5 |
15 |
| 1.0 |
9 |
| 1.5 |
8 |
| 2.0 |
10 |
| 2.5 |
7 |
| 3.0 |
14 |
| NA |
13 |
yyyy <- ggplot(data = data, mapping = aes(x = ev_T4, fill = factor(ev_T4))) + geom_histogram(binwidth = 0.5) #視覚化
ggplotly(yyyy) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc_onemonthの度数分布とヒストグラム
hsc_onemonth_count <- dplyr::count(data, hsc_onemonth)
knitr::kable(hsc_onemonth_count) #テーブル化
| 3.170833 |
1 |
| 3.533333 |
1 |
| 3.750000 |
1 |
| 3.887500 |
1 |
| 4.033333 |
1 |
| 4.100000 |
1 |
| 4.145833 |
1 |
| 4.262500 |
1 |
| 4.270833 |
1 |
| 4.279167 |
1 |
| 4.437500 |
1 |
| 4.441667 |
1 |
| 4.475000 |
1 |
| 4.504167 |
1 |
| 4.575000 |
2 |
| 4.600000 |
1 |
| 4.604167 |
1 |
| 4.645833 |
1 |
| 4.679167 |
1 |
| 4.750000 |
1 |
| 4.754167 |
1 |
| 4.762500 |
1 |
| 4.775000 |
1 |
| 4.804167 |
1 |
| 4.837500 |
1 |
| 4.883333 |
1 |
| 4.904167 |
1 |
| 4.912500 |
1 |
| 4.962500 |
1 |
| 4.966667 |
1 |
| 4.975000 |
1 |
| 5.008333 |
1 |
| 5.016667 |
1 |
| 5.037500 |
1 |
| 5.041667 |
1 |
| 5.058333 |
1 |
| 5.066667 |
1 |
| 5.079167 |
1 |
| 5.079167 |
1 |
| 5.116667 |
1 |
| 5.175000 |
1 |
| 5.200000 |
1 |
| 5.241667 |
1 |
| 5.270833 |
1 |
| 5.275000 |
1 |
| 5.287500 |
1 |
| 5.304167 |
2 |
| 5.329167 |
1 |
| 5.362500 |
1 |
| 5.383333 |
1 |
| 5.391667 |
1 |
| 5.400000 |
1 |
| 5.404167 |
1 |
| 5.420833 |
1 |
| 5.425000 |
1 |
| 5.533333 |
1 |
| 5.537500 |
1 |
| 5.554167 |
1 |
| 5.591667 |
1 |
| 5.608333 |
1 |
| 5.625000 |
1 |
| 5.637500 |
2 |
| 5.641667 |
1 |
| 5.650000 |
1 |
| 5.729167 |
1 |
| 5.737500 |
1 |
| 5.783333 |
1 |
| 5.795833 |
1 |
| 5.812500 |
1 |
| 5.829167 |
1 |
| 5.875000 |
1 |
| 5.879167 |
1 |
| 5.900000 |
1 |
| 5.987500 |
1 |
| 6.012500 |
1 |
| 6.029167 |
1 |
| 6.054167 |
1 |
| 6.095833 |
1 |
| 6.158333 |
1 |
| 6.237500 |
1 |
| 6.258333 |
1 |
| 6.570833 |
1 |
| 6.687500 |
1 |
| NA |
28 |
zzzz <- ggplot(data = data, mapping = aes(x = hsc_onemonth, fill = factor(hsc_onemonth))) + geom_histogram(binwidth = 0.3) + guides(fill = "none") #視覚化
ggplotly(zzzz) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb_onemonthの度数分布とヒストグラム
wb_onemonth_count <- dplyr::count(data, wb_onemonth)
knitr::kable(wb_onemonth_count) #テーブル化
| 1.15 |
1 |
| 1.25 |
1 |
| 1.30 |
1 |
| 1.40 |
1 |
| 1.65 |
1 |
| 1.75 |
1 |
| 1.80 |
1 |
| 1.85 |
1 |
| 1.95 |
1 |
| 2.00 |
1 |
| 2.00 |
1 |
| 2.10 |
2 |
| 2.15 |
1 |
| 2.20 |
1 |
| 2.25 |
2 |
| 2.30 |
1 |
| 2.30 |
1 |
| 2.35 |
1 |
| 2.40 |
3 |
| 2.45 |
1 |
| 2.45 |
2 |
| 2.50 |
3 |
| 2.55 |
1 |
| 2.60 |
1 |
| 2.70 |
1 |
| 2.75 |
1 |
| 2.75 |
1 |
| 2.80 |
4 |
| 2.80 |
1 |
| 2.85 |
4 |
| 2.85 |
2 |
| 2.85 |
2 |
| 2.90 |
1 |
| 2.90 |
1 |
| 2.95 |
2 |
| 2.95 |
3 |
| 3.05 |
4 |
| 3.05 |
1 |
| 3.15 |
1 |
| 3.20 |
1 |
| 3.30 |
1 |
| 3.35 |
2 |
| 3.40 |
1 |
| 3.45 |
1 |
| 3.45 |
1 |
| 3.50 |
1 |
| 3.55 |
1 |
| 3.60 |
2 |
| 3.60 |
1 |
| 3.65 |
1 |
| 3.75 |
1 |
| 3.80 |
1 |
| 3.80 |
1 |
| 3.85 |
1 |
| 4.05 |
1 |
| 4.10 |
1 |
| 4.20 |
2 |
| 4.25 |
2 |
| 4.40 |
1 |
| 4.45 |
2 |
| 4.55 |
1 |
| 4.85 |
1 |
| NA |
26 |
A <- ggplot(data = data, mapping = aes(x = wb_onemonth, fill = factor(wb_onemonth))) + geom_histogram(binwidth = 0.3) + guides(fill = "none") #視覚化
ggplotly(A) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#ev_onemonthの度数分布とヒストグラム
ev_onemonth_count <- dplyr::count(data, ev_onemonth)
knitr::kable(ev_onemonth_count) #テーブル化
| -1.625 |
1 |
| -1.125 |
1 |
| -0.875 |
1 |
| -0.750 |
2 |
| -0.625 |
1 |
| -0.500 |
1 |
| -0.375 |
1 |
| -0.250 |
3 |
| -0.125 |
2 |
| 0.000 |
4 |
| 0.125 |
6 |
| 0.250 |
7 |
| 0.375 |
2 |
| 0.500 |
4 |
| 0.625 |
2 |
| 0.750 |
6 |
| 0.875 |
3 |
| 1.000 |
2 |
| 1.125 |
2 |
| 1.250 |
3 |
| 1.375 |
5 |
| 1.500 |
5 |
| 1.625 |
3 |
| 1.750 |
4 |
| 1.875 |
3 |
| 2.125 |
2 |
| 2.250 |
2 |
| 2.500 |
1 |
| 2.625 |
2 |
| 2.750 |
1 |
| 2.875 |
2 |
| NA |
30 |
B <- ggplot(data = data, mapping = aes(x = ev_onemonth, fill = factor(ev_onemonth))) + geom_histogram(binwidth = 0.3) + guides(fill = "none") #視覚化
ggplotly(B) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
1-4. 記述統計量
#hsc_T1
hsc_T1_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
hsc1.T1.mean = mean (hsc1_T1), #hsc1_T1の平均
hsc1.T1.sd = sd (hsc1_T1), #hsc1_T1のSD
hsc2.T1.mean = mean (hsc2_T1),
hsc2.T1.sd = sd (hsc2_T1),
hsc3.T1.mean = mean (hsc3_T1),
hsc3.T1.sd = sd (hsc3_T1),
hsc4.T1.mean = mean (hsc4_T1),
hsc4.T1.sd = sd (hsc4_T1),
hsc5.T1.mean = mean (hsc5_T1),
hsc5.T1.sd = sd (hsc5_T1),
hsc6.T1.mean = mean (hsc6_T1),
hsc6.T1.sd = sd (hsc6_T1),
hsc7.T1.mean = mean (hsc7_T1),
hsc7.T1.sd = sd (hsc7_T1),
hsc8.T1.mean = mean (hsc8_T1),
hsc8.T1.sd = sd (hsc8_T1),
hsc9.T1.mean = mean (hsc9_T1),
hsc9.T1.sd = sd (hsc9_T1),
hsc10.T1.mean = mean (hsc10_T1),
hsc10.T1.sd = sd (hsc10_T1),
hsc11.T1.mean = mean (hsc11_T1),
hsc11.T1.sd = sd (hsc11_T1),
hsc12.T1.mean = mean (hsc4_T1),
hsc12.T1.sd = sd (hsc4_T1),
eoe.mean.T1 = mean (eoe_T1),
eoe.sd.T1 = sd (eoe_T1),
lst.mean.T1 = mean (lst_T1),
lst.sd.T1 = sd (lst_T1),
aes.mean.T1 = mean (aes_T1),
aes.sd.T1 = sd (aes_T1),
hsc.mean.T1 = mean (hsc_T1),
hsc.sd.T1 = sd (hsc_T1))
knitr::kable(hsc_T1_discriptive, digits = 2) #出力
| 79 |
4.68 |
1.38 |
4.75 |
1.55 |
5.7 |
1.22 |
4.52 |
1.7 |
6.25 |
0.85 |
4.95 |
1.35 |
4.68 |
1.82 |
4.15 |
1.46 |
3.59 |
1.42 |
6.52 |
0.78 |
4.97 |
1.52 |
4.52 |
1.7 |
4.58 |
0.95 |
4.86 |
1.39 |
5.79 |
0.6 |
5.08 |
0.73 |
#hsc_T2
hsc_T2_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
hsc1.T2.mean = mean (hsc1_T2), #hsc1_T2の平均
hsc1.T2.sd = sd (hsc1_T2), #hsc1_T2のSD
hsc2.T2.mean = mean (hsc2_T2),
hsc2.T2.sd = sd (hsc2_T2),
hsc3.T2.mean = mean (hsc3_T2),
hsc3.T2.sd = sd (hsc3_T2),
hsc4.T2.mean = mean (hsc4_T2),
hsc4.T2.sd = sd (hsc4_T2),
hsc5.T2.mean = mean (hsc5_T2),
hsc5.T2.sd = sd (hsc5_T2),
hsc6.T2.mean = mean (hsc6_T2),
hsc6.T2.sd = sd (hsc6_T2),
hsc7.T2.mean = mean (hsc7_T2),
hsc7.T2.sd = sd (hsc7_T2),
hsc8.T2.mean = mean (hsc8_T2),
hsc8.T2.sd = sd (hsc8_T2),
hsc9.T2.mean = mean (hsc9_T2),
hsc9.T2.sd = sd (hsc9_T2),
hsc10.T2.mean = mean (hsc10_T2),
hsc10.T2.sd = sd (hsc10_T2),
hsc11.T2.mean = mean (hsc11_T2),
hsc11.T2.sd = sd (hsc11_T2),
hsc12.T2.mean = mean (hsc4_T2),
hsc12.T2.sd = sd (hsc4_T2),
eoe.mean.T2 = mean (eoe_T2),
eoe.sd.T2 = sd (eoe_T2),
lst.mean.T2 = mean (lst_T2),
lst.sd.T2 = sd (lst_T2),
aes.mean.T2 = mean (aes_T2),
aes.sd.T2 = sd (aes_T2),
hsc.mean.T2 = mean (hsc_T2),
hsc.sd.T2 = sd (hsc_T2))
knitr::kable(hsc_T2_discriptive, digits = 2) #出力
| 79 |
4.73 |
1.17 |
4.92 |
1.44 |
5.73 |
1.17 |
4.71 |
1.63 |
6.18 |
0.98 |
5.06 |
1.24 |
4.81 |
1.71 |
4.46 |
1.29 |
3.8 |
1.4 |
6.42 |
0.93 |
4.92 |
1.53 |
4.71 |
1.63 |
4.75 |
0.94 |
4.92 |
1.39 |
5.77 |
0.65 |
5.15 |
0.76 |
#hsc_T3
hsc_T3_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
hsc1.T3.mean = mean (hsc1_T3), #hsc1_T3の平均
hsc1.T3.sd = sd (hsc1_T3), #hsc1_T3のSD
hsc2.T3.mean = mean (hsc2_T3),
hsc2.T3.sd = sd (hsc2_T3),
hsc3.T3.mean = mean (hsc3_T3),
hsc3.T3.sd = sd (hsc3_T3),
hsc4.T3.mean = mean (hsc4_T3),
hsc4.T3.sd = sd (hsc4_T3),
hsc5.T3.mean = mean (hsc5_T3),
hsc5.T3.sd = sd (hsc5_T3),
hsc6.T3.mean = mean (hsc6_T3),
hsc6.T3.sd = sd (hsc6_T3),
hsc7.T3.mean = mean (hsc7_T3),
hsc7.T3.sd = sd (hsc7_T3),
hsc8.T3.mean = mean (hsc8_T3),
hsc8.T3.sd = sd (hsc8_T3),
hsc9.T3.mean = mean (hsc9_T3),
hsc9.T3.sd = sd (hsc9_T3),
hsc10.T3.mean = mean (hsc10_T3),
hsc10.T3.sd = sd (hsc10_T3),
hsc11.T3.mean = mean (hsc11_T3),
hsc11.T3.sd = sd (hsc11_T3),
hsc12.T3.mean = mean (hsc4_T3),
hsc12.T3.sd = sd (hsc4_T3),
eoe.mean.T3 = mean (eoe_T3),
eoe.sd.T3 = sd (eoe_T3),
lst.mean.T3 = mean (lst_T3),
lst.sd.T3 = sd (lst_T3),
aes.mean.T3 = mean (aes_T3),
aes.sd.T3 = sd (aes_T3),
hsc.mean.T3 = mean (hsc_T3),
hsc.sd.T3 = sd (hsc_T3))
knitr::kable(hsc_T3_discriptive, digits = 2) #出力
| 79 |
4.78 |
1.17 |
5.08 |
1.54 |
6.06 |
1.1 |
4.97 |
1.49 |
6.32 |
1.01 |
5.32 |
1.39 |
4.63 |
1.88 |
4.67 |
1.49 |
3.87 |
1.5 |
6.57 |
0.69 |
4.86 |
1.55 |
4.97 |
1.49 |
4.91 |
1.01 |
4.97 |
1.43 |
5.93 |
0.61 |
5.27 |
0.77 |
#hsc_T4
hsc_T4_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
hsc1.T4.mean = mean (hsc1_T4), #hsc1_T4の平均
hsc1.T4.sd = sd (hsc1_T4), #hsc1_T4のSD
hsc2.T4.mean = mean (hsc2_T4),
hsc2.T4.sd = sd (hsc2_T4),
hsc3.T4.mean = mean (hsc3_T4),
hsc3.T4.sd = sd (hsc3_T4),
hsc4.T4.mean = mean (hsc4_T4),
hsc4.T4.sd = sd (hsc4_T4),
hsc5.T4.mean = mean (hsc5_T4),
hsc5.T4.sd = sd (hsc5_T4),
hsc6.T4.mean = mean (hsc6_T4),
hsc6.T4.sd = sd (hsc6_T4),
hsc7.T4.mean = mean (hsc7_T4),
hsc7.T4.sd = sd (hsc7_T4),
hsc8.T4.mean = mean (hsc8_T4),
hsc8.T4.sd = sd (hsc8_T4),
hsc9.T4.mean = mean (hsc9_T4),
hsc9.T4.sd = sd (hsc9_T4),
hsc10.T4.mean = mean (hsc10_T4),
hsc10.T4.sd = sd (hsc10_T4),
hsc11.T4.mean = mean (hsc11_T4),
hsc11.T4.sd = sd (hsc11_T4),
hsc12.T4.mean = mean (hsc4_T4),
hsc12.T4.sd = sd (hsc4_T4),
eoe.mean.T4 = mean (eoe_T4),
eoe.sd.T4 = sd (eoe_T4),
lst.mean.T4 = mean (lst_T4),
lst.sd.T4 = sd (lst_T4),
aes.mean.T4 = mean (aes_T4),
aes.sd.T4 = sd (aes_T4),
hsc.mean.T4 = mean (hsc_T4),
hsc.sd.T4 = sd (hsc_T4))
knitr::kable(hsc_T4_discriptive, digits = 2) #出力
| 79 |
4.82 |
1.15 |
5.05 |
1.5 |
6.11 |
1.07 |
4.86 |
1.65 |
6.24 |
1.06 |
5.09 |
1.37 |
4.8 |
1.86 |
4.71 |
1.43 |
3.92 |
1.48 |
6.59 |
0.69 |
4.82 |
1.63 |
4.86 |
1.65 |
4.85 |
1.11 |
4.94 |
1.45 |
5.94 |
0.67 |
5.24 |
0.78 |
#hsc_onemonth
hsc_onemonth_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
hsc1.onemonth.mean = mean (hsc_onemonth), #hsc_onemonthの平均
hsc1.onemonth.sd = sd (hsc_onemonth)) #hsc_onemonthのSD
knitr::kable(hsc_onemonth_discriptive, digits = 2) #出力
#wb_T1
wb_T1_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
wb1.T1.mean = mean (wb1_T1), #hsc1_T10の平均
wb1.T1.sd = sd (wb1_T1), #hsc1_T10のSD
wb2.T1.mean = mean (wb2_T1),
wb2.T1.sd = sd (wb2_T1),
wb3.T1.mean = mean (wb3_T1),
wb3.T1.sd = sd (wb3_T1),
wb4.T1.mean = mean (wb4_T1),
wb4.T1.sd = sd (wb4_T1),
wb5.T1.mean = mean (wb5_T1),
wb5.T1.sd = sd (wb5_T1),
wb.T1.mean = mean (wb_T1),
wb.T1.sd = sd (wb_T1))
knitr::kable(wb_T1_discriptive, digits = 2) #出力
| 79 |
3.22 |
1 |
2.87 |
1.14 |
3.04 |
1.11 |
2.23 |
1.17 |
2.86 |
1.14 |
2.84 |
0.8 |
#wb_T2
wb_T2_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
wb1.T2.mean = mean (wb1_T2), #hsc1_T2の平均
wb1.T2.sd = sd (wb1_T2), #hsc1_T2のSD
wb2.T2.mean = mean (wb2_T2),
wb2.T2.sd = sd (wb2_T2),
wb3.T2.mean = mean (wb3_T2),
wb3.T2.sd = sd (wb3_T2),
wb4.T2.mean = mean (wb4_T2),
wb4.T2.sd = sd (wb4_T2),
wb5.T2.mean = mean (wb5_T2),
wb5.T2.sd = sd (wb5_T2),
wb.T2.mean = mean (wb_T2),
wb.T2.sd = sd (wb_T2))
knitr::kable(wb_T2_discriptive, digits = 2) #出力
| 79 |
3.22 |
1 |
3.13 |
1.11 |
3.15 |
1.17 |
2.38 |
1.29 |
2.94 |
1.24 |
2.96 |
0.93 |
#wb_T3
wb_T3_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
wb1.T3.mean = mean (wb1_T3), #hsc1_T3の平均
wb1.T3.sd = sd (wb1_T3), #hsc1_T3のSD
wb2.T3.mean = mean (wb2_T3),
wb2.T3.sd = sd (wb2_T3),
wb3.T3.mean = mean (wb3_T3),
wb3.T3.sd = sd (wb3_T3),
wb4.T3.mean = mean (wb4_T3),
wb4.T3.sd = sd (wb4_T3),
wb5.T3.mean = mean (wb5_T3),
wb5.T3.sd = sd (wb5_T3),
wb.T3.mean = mean (wb_T3),
wb.T3.sd = sd (wb_T3))
knitr::kable(wb_T3_discriptive, digits = 2) #出力
| 79 |
3.35 |
1.16 |
3.04 |
1.24 |
3.28 |
1.12 |
2.32 |
1.25 |
2.68 |
1.16 |
2.93 |
0.92 |
#wb_T4
wb_T4_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
wb1.T4.mean = mean (wb1_T4), #hsc1_T4の平均
wb1.T4.sd = sd (wb1_T4), #hsc1_T4のSD
wb2.T4.mean = mean (wb2_T4),
wb2.T4.sd = sd (wb2_T4),
wb3.T4.mean = mean (wb3_T4),
wb3.T4.sd = sd (wb3_T4),
wb4.T4.mean = mean (wb4_T4),
wb4.T4.sd = sd (wb4_T4),
wb5.T4.mean = mean (wb5_T4),
wb5.T4.sd = sd (wb5_T4),
wb.T4.mean = mean (wb_T4),
wb.T4.sd = sd (wb_T4))
knitr::kable(wb_T4_discriptive, digits = 2) #出力
| 79 |
3.18 |
1.11 |
2.91 |
1.21 |
3.15 |
1.22 |
2.33 |
1.32 |
2.89 |
1.37 |
2.89 |
1.03 |
#wb_onemonth
wb_onemonth_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
wb.onemonth.mean = mean (wb_onemonth), #wb_onemonthの平均
wb.onemonth.sd = sd (wb_onemonth)) #wb_onemonthのSD
knitr::kable(wb_onemonth_discriptive, digits = 2) #出力
#ev_T1
ev_T1_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
ev1.T1.mean = mean (ev1_T1), #ev1_T1の平均
ev1.T1.sd = sd (ev1_T1), #ev1_T1のSD
ev2.T1.mean = mean (ev2_T1),
ev2.T1.sd = sd (ev2_T1),
ev.T1.mean = mean (ev_T1),
ev.T1.sd = sd (ev_T1))
knitr::kable(ev_T1_discriptive, digits = 2) #出力
| 79 |
1.03 |
2.36 |
1.03 |
2.42 |
1.03 |
1.6 |
#ev_T2
ev_T2_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
ev1.T2.mean = mean (ev1_T2), #ev1_T2の平均
ev1.T2.sd = sd (ev1_T2), #ev1_T2のSD
ev2.T2.mean = mean (ev2_T2),
ev2.T2.sd = sd (ev2_T2),
ev.T2.mean = mean (ev_T2),
ev.T2.sd = sd (ev_T2))
knitr::kable(ev_T2_discriptive, digits = 2) #出力
| 79 |
1.29 |
2.27 |
0.2 |
2.48 |
0.75 |
1.62 |
#ev_T3
ev_T3_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
ev1.T3.mean = mean (ev_T3), #ev1_T3の平均
ev1.T3.sd = sd (ev1_T3), #ev1_T3のSD
ev2.T3.mean = mean (ev2_T3),
ev2.T3.sd = sd (ev2_T3),
ev.T3.mean = mean (ev_T3),
ev.T3.sd = sd (ev_T3))
knitr::kable(ev_T3_discriptive, digits = 2) #出力
| 79 |
0.73 |
2.38 |
0.53 |
2.43 |
0.73 |
1.58 |
#ev_T4
ev_T4_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
ev1.T4.mean = mean (ev1_T4), #ev1_T4の平均
ev1.T4.sd = sd (ev1_T4), #ev1_T4のSD
ev2.T4.mean = mean (ev2_T4),
ev2.T4.sd = sd (ev2_T4),
ev.T4.mean = mean (ev_T4),
ev.T4.sd = sd (ev_T4))
knitr::kable(ev_T4_discriptive, digits = 2) #出力
| 79 |
1.33 |
2.27 |
0.52 |
2.3 |
0.92 |
1.47 |
#ev_onemonth
ev_onemonth_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
ev.onemonth.mean = mean (ev_onemonth), #ev_onemonthの平均
ev.onemonth.sd = sd (ev_onemonth)) #ev_onemonthのSD
knitr::kable(ev_onemonth_discriptive, digits = 2) #出力
#age_T1
age_T1_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
age.T1.mean = mean (age_T1), #age_T1の平均
age.T1.sd = sd (age_T1)) #age_T1のSD
knitr::kable(age_T1_discriptive, digits = 2) #出力
#age_T2
age_T2_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
age.T2.mean = mean (age_T2), #age_T2の平均
age.T2.sd = sd (age_T2)) #age_T2のSD
knitr::kable(age_T2_discriptive, digits = 2) #出力
#age_T3
age_T3_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
age.T3.mean = mean (age_T3), #age_T3の平均
age.T3.sd = sd (age_T3)) #age_T3のSD
knitr::kable(age_T3_discriptive, digits = 2) #出力
#age_T4
age_T4_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
age.T4.mean = mean (age_T4), #age_T4の平均
age.T4.sd = sd (age_T4)) #age_T4のSD
knitr::kable(age_T4_discriptive, digits = 2) #出力